18F-fluorodeoxyglucose positron-emission tomography-computed tomography to diagnose recurrent cancer
Bibliographic record
Abstract
BACKGROUND: Sometimes the diagnosis of recurrent cancer in patients with a previous malignancy can be challenging. This prospective cohort study assessed the clinical utility of (18)F-fluorodeoxyglucose positron-emission tomography-computed tomography ((18)F-FDG PET-CT) in the diagnosis of clinically suspected recurrence of cancer. METHODS: Patients were eligible if cancer recurrence (non-small-cell lung (NSCL), breast, head and neck, ovarian, oesophageal, Hodgkin's or non-Hodgkin's lymphoma) was suspected clinically, and if conventional imaging was non-diagnostic. Clinicians were asked to indicate their management plan before and after (18)F-FDG PET-CT scanning. The primary outcome was change in planned management after (18)F-FDG PET-CT. RESULTS: Between April 2009 and June 2011, 101 patients (age, median 65 years; 55% female) were enroled from four cancer centres in Ontario, Canada. Distribution by primary tumour type was: NSCL (55%), breast (19%), ovarian (10%), oesophageal (6%), lymphoma (6%), and head and neck (4%). Of the 99 subjects who underwent (18)F-FDG PET-CT, planned management changed after (18)F-FDG PET-CT in 52 subjects (53%, 95% confidence interval (CI), 42-63%); a major change in plan from no treatment to treatment was observed in 38 subjects (38%, 95% CI, 29-49%), and was typically associated with (18)F-FDG PET-CT findings that were positive for recurrent cancer (37 subjects). After 3 months, the stated post-(18)F-FDG PET-CT management plan was actually completed in 88 subjects (89%, 95% CI, 81-94%). CONCLUSION: In patients with suspected cancer recurrence and conventional imaging that is non-diagnostic, (18)F-FDG PET-CT often provides new information that leads to important changes in patient management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".